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nigam
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nigam
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-We analyze multiple types of health data (EHR, Claims, Wearables, Weblogs, and Patient blogs), to answer clinical questions, generate insights, and build predictive models for the learning health system[[int:more_details|More details here.]]+We analyze multiple types of health data (EHR, Claims, Wearables, Weblogs, and Patient blogs), in service of the learning health system ([[:more_details|see examples]]).
  
-  * We **enable better medical decisions** and **answer clinical questions** using EHR and Claims data, via a bedside consult service that implements the vision of [[http://content.healthaffairs.org/content/33/7/1229.abstract |a ‘Green Button’]] for using aggregate patient data at the [[http://stanmed.stanford.edu/2016winter/on-the-button.html | point of care]]. Check out our [[ http://greenbutton.stanford.edu | Informatics Consult Service]] that puts this idea in action. +We **answer clinical questions** using aggregate patient data at the [[http://stanmed.stanford.edu/2016winter/on-the-button.html|bedside]]. The [[:greenbutton| Informatics Consult Service]] put this [[https://shahlab.stanford.edu/greenbutton_ideaidea]] in action and led to the creation of [[https://www.atroposhealth.com/| Atropos Health]].
-  * We **make predictions** that allow taking mitigating actions, and also study the [[ https://www.nejm.org/doi/full/10.1056/NEJMp1714229| ethical implications]] of using machine learning in clinical care. We have built models for predicting [[http://bmjopen.bmj.com/cgi/content/full/bmjopen-2016-011580?ijkey=oCxNIjOhCzOdmR8&keytype=ref | future increases in cost]], identifying [[http://www.ncbi.nlm.nih.gov/pubmed/26606167 | slow healing wounds]], [[http://www.ncbi.nlm.nih.gov/pubmed/24988898|missed diagnoses of depression]] and for [[http://stanmed.stanford.edu/2018summer/artificial-intelligence-puts-humanity-health-care.html improving palliative care]]+
-  * We also develop innovative methods to analyze multiple datatypes to **generate insights**. For example, learning effective treatment pathways in Type 2 Diabetes in an [[ http://www.ohdsi.org | open collaborative research network]] using [[https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2698083 | claims data from multiple countries]]. Learning reference intervals of laboratory tests from a [[http://www.ncbi.nlm.nih.gov/pubmed/26707631 |clinical data warehouse]]. Monitoring Point-of-Care glucose meters  using [[http://www.ncbi.nlm.nih.gov/pubmed/26988586 |coincident testing]] with central laboratory blood glucose measurements. Detecting skin adverse reactions by analyzing content in a [[https://jamanetwork.com/journals/jamaoncology/fullarticle/2673831 | health social network]]. Finding drug adverse events, and drug-drug interactions using [[http://www.ncbi.nlm.nih.gov/pubmed/23571773| using clinical notes]]. Mining user [[https://www.ncbi.nlm.nih.gov/pubmed/27655225 | search logs]] to predict health utilization, and analyzing [[https://www.ncbi.nlm.nih.gov/pubmed/26293444 | information seeking behavior]] of health professionals. Inferring physical function from [[ https://www.ncbi.nlm.nih.gov/pubmed/30394876 wearables data]], and personalizing ICU [[ https://www.ncbi.nlm.nih.gov/pubmed/29218906 | alarm thresholds]]. Assessing [[https://www.ncbi.nlm.nih.gov/pubmed/29557976 | impact of informatics tools]] and databases+
  
-**About us**: [[:Lab members]] \\ +We **make predictions** that allow taking mitigating actions, [[https://stanmed.stanford.edu/artificial-intelligence-puts-humanity-health-care/|keeping the human in the loop]]. The [[:aihcProgram for AI in Healthcare]] conducts the research which the [[:datascienceApplied Data Science team]] puts into practice.
-**Internal** (log in required): [[int:Onboarding|New Lab members]], [[int:Lab information]], [[int:Lab communication]], [[int:Projects]], [[int:rotation_projects|Rotations]][[:onboarding_affiliates|For Collaborators]][[archive:|Archived pages]] \\ +
-**Teaching**: [[BIOMEDIN215|BIOMEDIN 215]] Autumn quarter of each year, [[:AIHC Bootcamp| AI in Healthcare Bootcamp]] \\+
  
-**Selected talks and videos**: \\ +We **develop methods** to analyze multiple datatypes for generating insights. Such as:
----- +
-{{youtube>Njphqhju5Fo?small | Supporting clinical decision making at the bedside}} Supporting clinical decision making at the bedside +
-----+
  
-{{youtube>zRwKm-Uhkiw?small Informatics Consult Service @ Stanford}} Informatics Consult Service at Stanford +   * Identifying [[https://www.sciencedirect.com/science/article/pii/S2213260018305083|biomarkers for poor outcomes in fibrotic diseases]], learning [[http://www.ncbi.nlm.nih.gov/pubmed/26707631| reference intervals of laboratory tests]] and [[http://www.ncbi.nlm.nih.gov/pubmed/26988586| monitoring Point-of-Care glucose meters]] using routine laboratory testing data. 
-----+  * Detecting skin adverse reactions by analyzing content in a [[https://jamanetwork.com/journals/jamaoncology/fullarticle/2673831|health social network]], enabling [[https://pubmed.ncbi.nlm.nih.gov/31583282/|medical device surveillance]], discovering drug adverse events as well as drug-drug interactions [[http://www.ncbi.nlm.nih.gov/pubmed/23571773| from clinical notes]] using novel methods for [[https://hai.stanford.edu/news/agile-nlp-clinical-text-covid-19-and-beyond|processing textual documents]]. 
 +  * Inferring physical function from [[https://www.ncbi.nlm.nih.gov/pubmed/30394876|wearables data]], predicting healthcare utilization from [[https://www.ncbi.nlm.nih.gov/pubmed/27655225|Web search logs]] and understanding [[https://www.ncbi.nlm.nih.gov/pubmed/26293444| information seeking behavior]] of health professionals.
  
-{{youtube>gQu2HbusrGQ?small |  +**About us**: [[:lab_members|Lab members]], [[:jobs| Open positions]] \\ 
-Keeping the Human in the Loop for Equitable and Fair Use of ML in Healthcare}} Keeping the Human in the Loop for Equitable and Fair Use of ML in Healthcare, at AIMiE 2018 +**Internal**  (log in required): [[:int:onboarding|On boarding]], [[:int:compute_resources|Compute Resources]], [[:int:lab_communication|Lab communication]], [[:int:projects|Projects]], [[:int:rotation_projects|Rotations]], [[:onboarding_affiliates|For Collaborators]], [[:archive:start|Archived pages]] 
-----+ 
 +==== Teaching ==== 
 + 
 +  * [[https://biomedin215.stanford.edu/|BIOMEDIN 215 Data Science for Medicine]], taught for the BMI Graduate program is designed to prepare you to pose and answer meaningful clinical questions using routinely collected healthcare data. 
 +  * [[https://explorecourses.stanford.edu/search?q=BIOMEDIN+225|BIOMEDIN 225 Data Science for Medicine]], taught for the MCiM program explores how to use electronic health records (EHRs) and other patient data in conjunction with recent advances in artificial intelligence (AI) and evolving business models to improve healthcare. 
 +  * [[https://www.coursera.org/specializations/ai-healthcare/|AI in Healthcare Specialization on Coursera]]which reviews the current and future applications of AI in healthcare with the goal of learning to bring AI technologies into the clinic safely and ethically. 
 +  * [[https://online.stanford.edu/courses/xbiomedin215-machine-learning-projects-healthcare|XBIOMEDIN215 Machine Learning Projects in Healthcare]], where you work through interactive exercises and case studies, attend live webinars, receive ongoing feedback from the course team, and collaborate with your fellow learners to gain the real-world skills you need to run your own machine learning projects. 
 +  * [[https://stanfordmlgroup.github.io/projects/aihc/|AI in Healthcare Bootcamp]], provides students an opportunity to do cutting-edge research at the intersection of AI and healthcare. 
 +  * Miscellaneous [[:other_talks|Talks]], [[:seminars|Seminars]]
  
-{{youtube>xW3drA3ijRc?small | Building a Machine Learning Healthcare System, at XLDB 2018}} Building a Machine Learning Healthcare System, at XLDB, April 30 2018 
 ---- ----
  
-{{youtube>2ERCBBQOMlg?small&start=460 | Performing an Informatics Consult, Grand rounds in Medicine at Stanford, Feb 1 2017}} Performing an Informatics Consult, Feb 1 2017 +<html<iframe src="https://slideslive.com/embed/presentation/38931909?auto_play=&zoom_ratio=&disable_fullscreen=&locale=en&demo=&vertical_enabled=true&vertical_enabled_on_mobile=&vertical_when_width_lte=500&allow_hidden_controls_when_paused=true&user_uuid=3760fd95-4c65-4d33-af8f-14b581de0e6c" width="1094" height="685" scrolling="no" frameborder="0" allowfullscreen=_ckgedit_QUOT__ckgedit> webkitallowfullscreen=_ckgedit_QUOT__ckgedit> mozallowfullscreen="" sandbox="allow-forms allow-pointer-lock allow-popups allow-same-origin allow-scripts allow-top-navigation allow-storage-access-by-user-activation" allow="autoplay, fullscreen" style="margin: 0px auto; display: block;"></iframe> </html> —- // 
-----+
  
-[[:Other talks]], [[:Seminars]] 
start.1544054659.txt.gz · Last modified: 2018/12/05 16:04 by nigam